Passive Data Link Layer 802.11 Wireless Device Driver Fingerprinting
By Jason Franklin, Damon McCoy. USENIX Security Symposium, 2006
295 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/54f5359ef8434116511a66bb0d9bed02f6bed38f
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- Kunal Sankhe
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Citations in the corpus
- Deep Learning Convolutional Neural Networks for Radio Identification (2018, methodology): “In [4], a passive fingerprinting technique is proposed that identifies the wireless device driver running on an IEEE 802.”
- ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks (2018, methodology): “Franklin et al [4] Master DB of signatures for wireless device driver fingerprinting”
- Wireless device identification with radiometric signatures (2008, methodology): “hardware id clock skew variation n/a Franklin et al. [ 13 ] Software meas.”
- Device Fingerprinting in Wireless Networks: Challenges and Opportunities (2015, background): “Franklin et al. [9] derived features based on the bin frequency of arrival time between probe request frames during active scanning.”
- IoT SENTINEL: Automated Device-Type Identification for Security Enforcement in IoT (2016, methodology): “In contrast to techniques aggregating network traffic statistics over a period of time [12], [21], this extraction method raises some issues for comparison, since fingerprints have variable size n .”
- AMOEBA: Robust Location Privacy Scheme for VANET (2007, background): “Next, the various constraints of vehicular networks applicable to the problems addressed in this paper are presented.”
- No NAT'd User Left Behind: Fingerprinting Users behind NAT from NetFlow Records Alone (2014, methodology): “The same issue applies to the work in [11], where a technique to accurately identify the software driver used by 802.”
- On Fast and Accurate Detection of Unauthorized Wireless Access Points Using Clock Skews (2008, methodology): “[22] introduced a technique to fingerprint wireless device drivers.”
- AccelPrint: Imperfections of Accelerometers Make Smartphones Trackable (2014, methodology): “The combination of chipsets, firmware and device drivers [30], timing interval of probe request frames [19], or the patterns of wireless traffic [50] were also used to identify devices.”